Body fat scale and control method thereof

Abstract

The invention provides a body fat scale and a control method thereof, the body fat scale comprises an identity recognition unit and a control unit, the identity recognition unit comprises a first fingerprint recognition module and a second fingerprint recognition module, and the first fingerprint recognition module comprises an angle-adjustable first fingerprint recognition panel; the second fingerprint identification module comprises a second fingerprint identification panel which forms a first inclination angle with the vertical plane, and the angle of each module accords with the natural angle of contact between the finger of the user and the body fat scale, thereby improving the user experience. The control unit is configured to determine a target fingerprint identification module adapted to a user; determining a first identifier corresponding to the first fingerprint image in response to a fingerprint input operation of a user through a target fingerprint identification module; and based on the first identifier, establishing a matching relationship between the first initial data and the basic data corresponding to the first identifier. In the use process of the user, the use habits and the like of the user are learned to adapt to operation characteristics of different users, and the user identity is quickly and accurately determined.

Description

Technical Field

[0001] The application relates to the field of electronic measurement, and in particular to a body fat scale and a control method thereof. Background Art

[0002] As people's living standards improve, they pay more and more attention to health and body management. In daily life, users use body fat scales to manage their health and body, helping people understand their bodies, such as weight, body fat percentage, BMI, visceral fat, muscle mass, etc.

[0003] In the related technology, body fat scales use communication means such as Bluetooth and Wi-Fi to establish connections with terminal devices such as smartphones and tablets, and then achieve collaborative work with applications on these terminal devices. In this process, users need to enter basic data such as height, gender, and age in the application; the body fat scale is equipped with pressure sensors and electrodes to measure the pressure value and electrical impedance value generated when the user stands on the scale. The user needs to manually select the user's basic data in the operation interface of the body fat scale or through the supporting application to achieve the matching association between the user's basic data and the measurement data, and then carry out subsequent data processing and determination of indicator data.

[0004] However, in traditional usage scenarios, when the body fat scale serves multiple users, the above manual selection of basic data for matching can easily lead to the inability to accurately identify the user, which in turn leads to inaccurate indicator data and analysis, resulting in a poor user experience. Summary of the invention

[0005] The present application provides a body fat scale and a control method thereof, which can quickly and accurately determine the identity of a user during use.

[0006] In a first aspect, a body fat scale is provided, comprising an identification unit, a measurement unit, a control unit and a wireless communication unit, wherein the control unit is in communication with the identification unit, the measurement unit and the wireless communication unit. The identification unit comprises a first fingerprint recognition module and a second fingerprint recognition module, wherein the first fingerprint recognition module is arranged on the first surface of the body fat scale, and the first fingerprint recognition module comprises a first fingerprint recognition panel with an adjustable angle and a first sensor; the second fingerprint recognition module is arranged on the side of the body fat scale, and the second fingerprint recognition module comprises a second fingerprint recognition panel and a second sensor, and the second fingerprint recognition panel has a first inclination angle with the plumb plane. The measurement unit comprises an electrode module and a pressure sensor module.

[0007] The control unit is configured to: when the body fat scale is used a preset number of times and it is recognized that the user is in a stable state on the body fat scale, obtain the user's first initial data, the first initial data including bio-impedance data and pressure distribution data; input the first initial data, historical usage data, and user preference analysis data into a preset recognition model, and determine a target fingerprint recognition module that is adapted to the user; in response to the user entering a fingerprint operation through the target fingerprint recognition module, obtain the user's first fingerprint image, and determine the first identifier corresponding to the first fingerprint image, wherein the target fingerprint recognition module is the first fingerprint recognition module or the second fingerprint recognition module; based on the first identifier, establish a matching relationship between the first initial data and the basic data corresponding to the first identifier, and determine the user's indicator data.

[0008] The body fat scale is equipped with multiple fingerprint recognition modules. The layout design fully considers ergonomics and usage scenarios. Some fingerprint recognition modules are set on the surface of the body fat scale, and some are hidden on the side of the body fat scale, providing more choices for users with different usage habits. In addition, when users use the body fat scale, it continuously learns the user's usage habits, user preference analysis data, etc., to adapt to the operating characteristics of different users, and guides users to enter fingerprints through the target fingerprint recognition module among multiple fingerprint recognition modules, so as to quickly and accurately determine the user's identity.

[0009] In a possible implementation, the first fingerprint recognition module further includes a universal connection mechanism, which is used to flexibly connect the first fingerprint recognition panel to the body fat scale. The first fingerprint recognition panel is provided with a first sensor, and the second fingerprint recognition panel is provided with a second sensor.

[0010] By setting a universal connection mechanism in the first fingerprint recognition module, the first fingerprint recognition panel is a panel with adjustable angle. The user can manually rotate the first fingerprint recognition panel in the first fingerprint recognition module to adjust the angle to meet the needs of users with different gestures or different users inputting fingerprint images through the first fingerprint recognition panel.

[0011] In a possible implementation, the universal connection mechanism also includes an angle sensor and a motor. After determining the target fingerprint recognition module adapted to the user, the control unit is further configured to: if the target fingerprint recognition module is the first fingerprint recognition module, analyze the historical usage data to determine the target angle of the first fingerprint recognition panel, wherein the historical usage data includes the frequency of use of the first fingerprint recognition module and the initial angle corresponding to the first fingerprint recognition panel obtained by the angle sensor when the user enters the fingerprint; and control the motor to adjust the angle of the first fingerprint recognition panel to the target angle.

[0012] It should be understood that the fingerprint recognition panel is driven to rotate by the motor-driven universal connection mechanism to achieve automatic angle adjustment. This structure can achieve multi-angle adjustment in a wide range to meet the needs of different users (such as different heights, different usage habits, etc.) to enter fingerprints in a comfortable posture. In addition, during use, the fingerprint recognition panel in the corresponding fingerprint recognition module can be adjusted to a suitable angle before the user enters the fingerprint.

[0013] In a possible implementation, after the control unit obtains the user's first fingerprint image in response to the user entering a fingerprint operation through the target fingerprint recognition module, it is also configured to: identify and analyze the first fingerprint image based on the user information stored in the body fat scale; if the first fingerprint image is not identified, perform a matching degree analysis on the features of the first fingerprint image and the fingerprint features collected by the first fingerprint recognition module and the second fingerprint recognition module to obtain the corresponding similarity; and use the fingerprint recognition module with the highest similarity as the updated target fingerprint recognition module.

[0014] After obtaining the first fingerprint image, there may still be unrecognized situations in the process of identifying and analyzing it. In order to improve the fault tolerance of the solution and enhance the user experience, if the first fingerprint image is not identified, the user is guided to enter the fingerprint through other fingerprint recognition modules through analysis.

[0015] In a possible implementation, when the number of times the body fat scale is used has not reached a preset number and it is recognized that the user is in a stable state on the body fat scale, the control unit is further configured to: obtain a second fingerprint image and second initial data of the user, wherein the second fingerprint image is an image collected by the first fingerprint recognition module and / or the second fingerprint recognition module; analyze the second fingerprint image and the second initial data to obtain the user's historical usage data and user preference analysis data; and optimize the preset recognition model based on the second fingerprint image, the second initial data, the historical usage data and the user preference analysis data to obtain an optimized preset recognition model.

[0016] In the daily use scenarios of body fat scales, the intelligence of body fat scales will gradually advance as the number of times users use them increases. Every time a user completes a measurement, the body fat scale will collect the second fingerprint image to accurately identify the user's identity. The body fat scale uses the second fingerprint image, the second initial data, the historical usage data, and the user preference analysis data as input data to fully optimize the preset recognition model. In the continuous iterative calculation, the preset recognition model gradually adjusts the internal parameters to improve the recognition accuracy of the user's individual characteristics and behavioral habits, thereby obtaining an optimized preset recognition model, and then in the subsequent use process, it provides users with a more accurate target fingerprint recognition model and provides users with personalized services.

[0017] In a possible implementation, the first fingerprint recognition module further includes a first indicator light, and the second fingerprint recognition module further includes a second indicator light; after the control unit determines the target fingerprint recognition module adapted to the user, it is further configured to: and generate prompt information, wherein the prompt information is used to instruct the user to enter the fingerprint through the target fingerprint recognition module. When the prompt information indicates that the first fingerprint recognition module is the target fingerprint recognition module, the first indicator light is driven to emit light based on a preset mode; when the prompt information indicates that the second fingerprint recognition module is the target fingerprint recognition module, the first indicator light is driven to emit light based on a preset mode; wherein the preset mode includes a constant light mode or a flashing mode.

[0018] The generated prompt information is used to guide the user to enter the fingerprint through the target fingerprint recognition module. The prompt information can be presented in different forms to achieve the purpose of prompting the user.

[0019] In one possible implementation, the first fingerprint recognition module also includes a first LED light source, and the first sensor includes an optical fingerprint sensor and a capacitive fingerprint sensor; the control unit is also configured to: obtain the user's heart rate data, the heart rate data is used to obtain the user's historical usage data, used to optimize the preset recognition model, and used to input into the preset recognition model to determine the target fingerprint recognition module adapted to the user.

[0020] Since more input data can cover more features and changes, the preset recognition model can learn more complex and subtle patterns and rules, reduce the risk of overfitting, and improve the generalization ability on unknown data to improve the accuracy of the preset recognition model. Therefore, without affecting the fingerprint recognition function, a special heart rate signal processing algorithm can be added to the control unit to filter, amplify, and extract features from the collected photoelectric volume pulse wave signal, calculate the user's heart rate data, and use the heart rate data as data for training, optimizing, and using the preset model.

[0021] In a possible implementation, the body fat scale includes a handle and a scale body; the identity recognition unit also includes a facial recognition module, the first fingerprint recognition module and the facial recognition module are arranged on the second surface of the handle, and the second fingerprint recognition module is arranged on the side of the scale body.

[0022] The body fat scale, which is divided into a handle and a scale body, is equipped with multiple fingerprint recognition modules, some of which are set on the surface of the handle, and some are hidden on the side of the scale body, providing more choices for users with different usage habits. This design ensures that no matter how the user steps on the body fat scale, no matter what their height, hand shape or finger features are, they can easily and comfortably place their fingers on one of the fingerprint recognition modules, greatly improving the convenience of obtaining fingerprint images and optimizing the user experience.

[0023] In a possible implementation, the first sensor includes an optical fingerprint sensor and a capacitive fingerprint sensor, and the second sensor includes an optical fingerprint sensor and a capacitive fingerprint sensor.

[0024] The first sensor and the second sensor serve as fusion sensors, combining the optical fingerprint sensor and the capacitive fingerprint sensor. The fusion of multiple sensors can not only improve the speed of fingerprint recognition, but also enhance the accuracy. Even when the finger is slightly stained or wet, the fingerprint image can be quickly and accurately acquired.

[0025] In a second aspect, a control method for a body fat scale is provided, which is applied to any body fat scale in the first aspect, and the control method includes: when the body fat scale is used a preset number of times and it is recognized that the user is in a stable state on the body fat scale, obtaining first initial data of the user, the first initial data including bio-impedance data and pressure distribution data; inputting the first initial data, historical usage data, and user preference analysis data into a preset recognition model, and determining a target fingerprint recognition module that is adapted to the user; in response to the user entering a fingerprint operation through the target fingerprint recognition module, obtaining a first fingerprint image of the user, and determining a first identifier corresponding to the first fingerprint image, wherein the target fingerprint recognition module is the first fingerprint recognition module or the second fingerprint recognition module; based on the first identifier, establishing a matching relationship between the first initial data and basic data corresponding to the first identifier, and determining the user's indicator data.

[0026] In a third aspect, a control device is provided, comprising a unit for executing any of the control methods of the body fat scale in the second aspect. The control device may be a body fat scale or a chip in the body fat scale.

[0027] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a control device, the control device executes any one of the body fat scale control methods in the second aspect.

[0028] In a fifth aspect, a computer program product is provided, the computer program product comprising: a computer program, when the computer program is executed by a control device, the control device executes any one of the body fat scale control methods in the second aspect.

[0029] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A schematic diagram showing a body fat scale and communication between the body fat scale and a terminal device is shown; Figure 2A schematic diagram of the system architecture of a body fat scale provided in an embodiment of the present application is shown; Figure 3 A schematic diagram of the structure of a body fat scale provided in an embodiment of the present application is shown; Figure 4 A structural schematic diagram of another body fat scale provided in an embodiment of the present application is shown; Figure 5 Show Figure 4 A schematic diagram of a body fat scale in another form; Figure 6 A schematic diagram showing a method of entering a fingerprint through a first fingerprint recognition module in a body fat scale provided in an embodiment of the present application is shown; Figure 7 A schematic diagram of entering a fingerprint through a first fingerprint recognition module in a body fat scale provided by an embodiment of the present application is shown; Figure 8 A schematic diagram of a first fingerprint recognition module in a body fat scale provided in an embodiment of the present application is shown; Fig. 9 A structural schematic diagram of another body fat scale provided in an embodiment of the present application is shown; Fig.10 A schematic flow chart of a method for controlling a body fat scale provided in an embodiment of the present application is shown; Fig.11 A schematic diagram of a flow chart of analysis in a control method of a body fat scale provided in an embodiment of the present application is shown; Fig.12 A schematic diagram of a control device of a body fat scale provided in an embodiment of the present application is shown; Fig.13 A structural schematic diagram of a body fat scale provided in the present application is shown. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. Among them, in the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can mean A or B; "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The terms "first", "second", and "third" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first", "second", and "third" may explicitly or implicitly include one or more of the features.

[0032] For the purpose of illustration rather than limitation, specific details such as specific system structures, technologies, etc. are provided to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0033] As people's living standards improve, they pay more and more attention to health and body management. In daily life, users use body fat scales to manage their health and body, helping people understand their bodies, such as weight, body fat percentage, visceral fat, muscle mass, etc.

[0034] Body fat scales are usually equipped with pressure sensors and electrodes for measuring multiple data. When a user steps on a body fat scale, the weight of the body will be evenly applied to the surface of the scale. The pressure sensor will be deformed by the pressure, causing the resistance or capacitance value to change. The electrical change is converted into an electrical signal, which is amplified, filtered, and processed to finally present the user's weight data in digital form. Also, when the user's feet, or feet and hands touch the electrodes of the body fat scale, the electrodes apply a tiny safe current to the user's body, which will circulate in the body through the user's tissues and body fluids; because fat tissue and muscle tissue have different impedances to current, the electrodes can detect the changes in electrical impedance generated when the current is conducted in different tissues of the user, and obtain the electrical impedance value.

[0035] In the related technology, the body fat scale uses communication means such as Bluetooth and Wi-Fi to establish a connection with terminal devices such as smartphones and tablets, and then achieves collaborative work with applications on these terminal devices. The application on the terminal device is used to collect basic data of the user, such as height, gender, age, etc., and transmit this basic data to the body fat scale for storage.

[0036] The following is a description of a body fat scale in the related art with reference to the accompanying drawings. Figure 1 A schematic diagram showing a body fat scale and communication between the body fat scale and a terminal device is shown; Figure 1 As shown in (a), the body fat scale can be a four-electrode body fat scale, which includes a measuring unit, a display unit, a wireless communication unit and a power supply unit, wherein the electrode module in the measuring unit includes four electrode sheets, which are arranged on the surface of the four-electrode body fat scale; Figure 1 As shown in (b), the body fat scale can also be an eight-electrode body fat scale, which includes a measuring unit, a display unit, a wireless communication unit, and a power supply unit, wherein the electrode module in the measuring unit includes eight electrode sheets, wherein four electrode sheets are arranged on the surface of the body of the eight-electrode body fat scale, and the other four electrode sheets are arranged on the surface of the handle of the eight-electrode body fat scale. It should also be understood that there is no limitation on the number of electrode sheets of the body fat scale.

[0037] It should be understood that the electrode sheet can be an external electrode sheet or an internal electrode sheet; Figure 1 As shown in (b) in FIG. 1 , the electrode sheet on the handle is an external electrode sheet. Figure 1 (a) Figure 1 As shown in (b), the electrode sheet on the scale body is a built-in electrode sheet.

[0038] like Figure 1 As shown in (c) in the figure, taking the eight-electrode body fat scale as an example, the eight-electrode body fat scale is connected to the mobile phone through wireless communication methods such as Bluetooth and Wi-Fi. When the eight-electrode body fat scale is used for the first time, the mobile phone and the eight-electrode body fat scale are connected through the application A on the mobile phone, and the basic data of user X and user Y are entered in the application A. When the eight-electrode body fat scale is used again, the basic data of each user transmitted by the mobile phone is received and stored in the memory of the eight-electrode body fat scale.

[0039] The body fat scale combines the measured weight data, electrical impedance value and other measurement data with the received basic data, and then uses a specific algorithm to accurately calculate the user's body fat rate, water content, muscle mass, bone mass and other indicators. These data are of great reference value for people to understand their own health status, formulate reasonable fitness plans, adjust diet structure, etc., and help users manage their health more scientifically.

[0040] It should be noted that the body fat scales in the related art do not have the technical ability to accurately identify users and cannot automatically determine the identity of the current user. When the body fat scale serves multiple users, the user needs to manually select the user's basic data in the body fat scale operation interface or through the supporting application to achieve the matching association between the user's basic data and the measurement data, and then carry out subsequent data processing and determination of indicator data.

[0041] In actual application scenarios, due to the lack of accurate user identification technology, manual selection operations increase the complexity of user use, reduce efficiency, and affect user experience; and are prone to user operation errors. Once the wrong information is selected, the matching of measurement data and users will be wrong, affecting the accuracy and availability of the data.

[0042] In view of this, the embodiment of the present application provides a body fat scale and a control method thereof, wherein the body fat scale includes an identification unit, a measurement unit, a control unit and a wireless communication unit, wherein the identification unit includes a first fingerprint identification module and a second fingerprint identification module, wherein the first fingerprint identification module includes a first fingerprint identification panel with an adjustable angle; the second fingerprint identification module includes a second fingerprint identification panel with a first inclination angle between the first and the vertical plane, and the angles of each module conform to the natural angle of contact between the user's finger and the body fat scale, thereby improving the user experience. The control unit is configured to obtain the user's first initial data when the body fat scale is used for a preset number of times and recognizes that the user is in a stable state on the body fat scale; input the first initial data, and the corresponding historical usage data and user preference analysis data into a preset recognition model to determine a target fingerprint identification module adapted to the user; in response to the user entering a fingerprint operation through the target fingerprint identification module, obtain the user's first fingerprint image and determine the first identifier corresponding to the first fingerprint image; based on the first identifier, establish a matching relationship between the first initial data and the basic data corresponding to the first identifier, learn the user's usage habits during the user's use, etc., so as to adapt to the operation characteristics of different users and quickly and accurately determine the user's identity.

[0043] Combine the following Figures 2 to 9 The body fat scale provided in the embodiment of the present application is described in detail, and the embodiment of the present application is illustrated by the following multiple exemplary embodiments.

[0044] Figure 2 A schematic diagram of the system architecture of a body fat scale provided in an embodiment of the present application is shown. Figure 2 As shown, the body fat scale 10 includes a control unit 11, a measuring unit 12, an identification unit 13, a storage unit 14, a display unit 15, a power supply unit 16 and a wireless communication unit 17. The control unit 11 is in communication connection with the measuring unit 12, the identification unit 13, the storage unit 14, the display unit 15, the power supply unit 16 and the wireless communication unit 17. Among them, the measuring unit 12 includes an electrode module 12A and a pressure sensor module 12B, etc.; the identification unit 13 includes a first fingerprint identification module 13A, a second fingerprint identification module 13B and a facial identification module 13C, etc.

[0045] It should be noted that Figure 2 The connection relationship between the units shown is only a schematic illustration and does not constitute a limitation on the connection relationship between the units in the body fat scale 10. In other embodiments of the present application, the body fat scale 10 may include Figure 2 More or fewer components than those shown, or the body fat scale 10 may include Figure 2 A combination of some of the components shown, or the body fat scale 10 may include Figure 2 Subassemblies of some of the components shown. Figure 2 The components shown may be implemented in hardware, software, or a combination of software and hardware.

[0046] The control unit 11 may include one or more processors, for example, the control unit may include a central processing unit (CPU), a graphics processing unit (GPU), etc. Among them, different processors may be independent devices or integrated into one processor.

[0047] The control unit may be the nerve center and command center of the body fat scale 10. The control unit may generate an operation control signal according to the instruction operation code and the timing signal to complete the control of fetching and executing instructions.

[0048] The measuring unit 12 includes an electrode module 12A and a pressure sensor module 12B. The measuring unit 12 is mainly used to measure the pressure value and electrical impedance value generated when the user stands on the scale.

[0049] The electrode module 12A mainly measures the user's body composition data such as fat content through bioelectrical impedance analysis (BIA) technology. The electrode module 12A includes a plurality of electrode sheets, for example, four electrode sheets or eight electrode sheets.

[0050] The electrode sheet will send a weak safe current to the user. Because different tissues and components of the user (such as muscle, fat, water, etc.) have different impedances to the current, the resistance of fat tissue is higher, while the resistance of muscle tissue and water is relatively low. After the current passes through the user, the electrode sheet receives the feedback signal and calculates the user's electrical impedance value based on the conduction of the current in the user.

[0051] The pressure sensor module 12B is used to measure the stable pressure distribution data after the user steps on the body fat scale, and then obtain the corresponding pressure value.

[0052] In some embodiments, in order to increase the accuracy of pressure value measurement, the pressure sensor module 12B includes multiple pressure sensors, which are evenly arranged. They can be used to accurately obtain the user's pressure distribution data, and detect the pressure distribution of the user when measuring on the body, thereby inferring the user's center of gravity position, which is used to characterize the user's body balance state and posture habits, thereby improving measurement accuracy.

[0053] The identity recognition unit 13 includes a first fingerprint recognition module 13A and a second fingerprint recognition module 13B, wherein the first fingerprint recognition module 13A and the second fingerprint recognition module 13B are both used to collect fingerprint images of the user to facilitate identification of the user's identity.

[0054] It should be understood that in the embodiment of the present application, the number of the first fingerprint recognition modules 13A can be one or more, and the number of the second fingerprint recognition modules 13B can also be one or more, which is not limited in the embodiment of the present application.

[0055] The body fat scale provided in the embodiment of the present application is equipped with multiple fingerprint recognition modules, and the layout design fully considers ergonomics and usage scenarios. Some fingerprint recognition modules are set on the surface of the body fat scale, and some fingerprint recognition modules are hidden on the side of the body fat scale, providing more choices for users with different usage habits. This design ensures that no matter what posture the user steps on the body fat scale, no matter what their height, hand shape or finger features are, they can easily and comfortably place their fingers on one of the fingerprint recognition modules, greatly improving the convenience of obtaining fingerprint images and optimizing user experience.

[0056] The storage unit 14 is used to store the user's basic data, measurement data, user habits, preset recognition models, control algorithms, and the like.

[0057] The display unit 15 is mainly provided with a display, which is used to display the user's basic data, measurement data, index data, etc., and can also display the corresponding curve analysis chart of each data, etc. The display may be a color display, for example, a Thin Film Transistor Liquid Crystal Display (TFT color display).

[0058] Figure 3 A schematic diagram of the structure of a body fat scale provided in an embodiment of the present application is shown; Figure 3 As shown, the body fat scale is a four-electrode body fat scale 100. It should be understood that the four-electrode body fat scale 100 includes a control unit, an identification unit 110, a measuring unit, a display unit, a storage unit, a wireless communication unit and a power supply unit, etc.; wherein, the electrode module in the measuring unit includes four electrode sheets, which are arranged on the first surface 130 of the scale body in the four-electrode body fat scale 100.

[0059] The identification unit 110 includes a first fingerprint identification module 111 and a second fingerprint identification module 112. The first fingerprint identification module 111 is arranged on the first surface 130 of the body fat scale, and the first fingerprint identification module 111 includes a first fingerprint identification panel, which is an angle-adjustable panel, and the second fingerprint identification module 112 is arranged on the side of the body fat scale, and the second fingerprint identification module 112 includes a second fingerprint identification panel, and the second fingerprint identification panel has a first inclination angle with the vertical plane.

[0060] It should be understood that the plumb plane refers to the plane perpendicular to the horizontal plane, that is, the plane where the plumb line is located. It should be understood that any object that is perpendicular to the ground like a plumb line is the plumb direction, and the plane that is perpendicular to the horizontal plane and the plane that is inclined to the front and side is the plumb plane.

[0061] Figure 4 A structural schematic diagram of another body fat scale provided in an embodiment of the present application is shown; Figure 4 As shown, the body fat scale is an eight-electrode body fat scale 200 , and the eight-electrode body fat scale 200 is structurally divided into a handle 210 and a scale body 220 .

[0062] The eight-electrode body fat scale 200 is divided into units, including a control unit, an identification unit, a measuring unit, a display unit, a storage unit, a wireless communication unit and a power supply unit; wherein, the electrode module in the measuring unit includes eight electrode sheets, four electrode sheets are arranged on the surface of the scale body 220, and the other four electrode sheets are arranged on the surface of the handle 210.

[0063] The identification unit includes a first fingerprint identification module 211 and a second fingerprint identification module 221. The first fingerprint identification module 211 is arranged on the surface of the handle. It should be understood that when the eight-electrode body fat scale 200 is in Figure 4 In the form shown, the surface of the scale body 220 and the surface of the handle 210 are on the same horizontal plane, which can also be understood as the first fingerprint recognition module 211 being arranged on the first surface of the body fat scale. The first fingerprint recognition module 211 includes a first fingerprint recognition panel, which is an angle-adjustable panel. The second fingerprint recognition module 221 is arranged on the side of the scale body, and the second fingerprint recognition module 221 includes a second fingerprint recognition panel, which has a first inclination angle with the plumb plane.

[0064] For the sake of convenience, the following embodiments of this application all take the body fat scale as an eight-electrode body fat scale as an example. Figure 5 Show Figure 4 A schematic diagram of a body fat scale in another form, such as Figure 5 As shown, the handle 210 and the scale body 220 in the eight-electrode body fat scale 200 are connected by a retractable cable, which is used for structural connection and communication connection. Figure 5 In the form shown, the surface of the scale body 220 and the surface of the handle 210 are not in the same horizontal plane, but are respectively in two intersecting planes. Therefore, the user can hold both sides of the handle 210 with both hands and raise the handle to the front or chest; at this time, the surface of the handle 210 with the display screen is used as the first surface, and the surface of the scale body 220 is used as the second surface.

[0065] It should be understood that in order to realize the first fingerprint recognition panel as a panel with adjustable angle, the first fingerprint recognition module also includes a universal connection mechanism, which is used to movably connect the first fingerprint recognition panel to the body fat scale, and the first fingerprint recognition panel is provided with a first sensor.

[0066] Figure 6 FIG. 1 shows a schematic diagram of recording fingerprints through the first fingerprint recognition module in the body fat scale provided in an embodiment of the present application, taking the user recording the fingerprint image through the left thumb as an example, as shown in FIG. Figure 6 As shown in (a) of FIG. 1 , the user's left thumb faces the first surface of the handle 210 , and at this time, the first fingerprint recognition panel is relatively parallel to the first surface; Figure 6 As shown in (b), the user's left thumb faces the side of the handle 210 that is away from the display. At this time, there is a second inclination angle between the first fingerprint recognition panel and the first surface, which is convenient for the user to enter the fingerprint image. In other words, a universal connection mechanism is added to the first fingerprint recognition module to achieve the angle adjustment of the first fingerprint recognition panel at a vertical angle.

[0067] It should be understood that different users have different dominant hands. Some users are accustomed to using their left hands, while some users are accustomed to using their right hands. Therefore, the universal connection mechanism in the first fingerprint recognition module can also adjust the first fingerprint recognition panel to the left and right in the horizontal direction.

[0068] Figure 7 FIG. 1 shows a schematic diagram of recording fingerprints through the first fingerprint recognition module in the body fat scale provided in an embodiment of the present application, taking the user recording the fingerprint image through the right thumb as an example, as shown in FIG. Figure 7 As shown in (a) of FIG. 1 , the user's right thumb faces the first surface of the handle 210 , and at this time, the first fingerprint recognition panel is relatively parallel to the first surface; Figure 7 As shown in (b), the user's right thumb faces the side of the handle 210 that is away from the display. At this time, there is a second inclination angle between the first fingerprint recognition panel and the first surface, which is convenient for the user to enter the fingerprint image. In other words, a universal connection mechanism is added to the first fingerprint recognition module to achieve the angle adjustment of the first fingerprint recognition panel at a vertical angle.

[0069] It should be understood that the user can manually rotate the fingerprint recognition panel (ie, the first fingerprint recognition panel or the second fingerprint recognition panel) in each fingerprint recognition module (ie, the first fingerprint recognition module or the second fingerprint recognition module) to adjust it to an angle suitable for the user.

[0070] Among them, the universal connection mechanism can be one of the structures such as a universal shaft, a spherical joint structure, a cross-axis universal joint, a ball cage universal joint, etc. The universal connection structure enables the first fingerprint recognition panel to achieve relatively free rotation within a larger angle range to meet the needs of users to input fingerprint images through the first fingerprint recognition panel under different gestures. Different users can enter fingerprint images through the first fingerprint recognition panel and the corresponding first sensor at an angle comfortable for their fingers.

[0071] Figure 8 A schematic diagram showing a first fingerprint recognition module in a body fat scale provided in an embodiment of the present application is shown. Figure 5 The cross-sectional view at HH´ is shown in Figure 8 As shown, the first fingerprint recognition module 211 includes a first fingerprint recognition panel 211A and a spherical joint structure 211C. The spherical joint structure 211C is used to connect the first fingerprint recognition panel 211A with an adjustable angle in the body fat scale, that is, the spherical joint structure 211C is used to connect the first fingerprint recognition panel 211A with an adjustable angle in the handle.

[0072] It should be understood that the first fingerprint recognition module may also include a fixed base, a rotating shaft, and a first fingerprint recognition panel; the fixed base is used to fix the entire device on the body fat scale, and the rotating shaft connects the fixed base and the fingerprint recognition panel body so that the panel can rotate around the axis.

[0073] In some embodiments, the universal connection mechanism further includes an angle sensor, and the angle sensor is used to accurately measure and feedback the rotation angle of the first fingerprint recognition panel.

[0074] In some embodiments, the universal connection mechanism further includes an angle sensor and a motor, wherein the angle sensor is used to accurately measure and feedback the rotation angle of the first fingerprint recognition panel, and the motor is used to adjust the universal connection mechanism to rotate according to the angle command issued by the control unit.

[0075] It should be understood that the fingerprint recognition panel is driven to rotate by a motor-driven universal connection mechanism to achieve automatic angle adjustment. This structure can achieve multi-angle adjustment in a wide range to meet the needs of different users (such as different heights, different usage habits, etc.) to enter fingerprints in a comfortable posture.

[0076] In the embodiment of the present application, the first fingerprint recognition panel is designed to be adjustable in angle so that the first fingerprint recognition module is arranged at Figure 3 The surface of the body fat scale shown is still set as Figure 4 The surface of the handle in the body fat scale shown allows the user to easily place his finger on the fingerprint recognition module, reducing the difficulty and time of the operation and improving the efficiency of obtaining the fingerprint image.

[0077] like Figure 3 , Figure 4 As shown, the second fingerprint recognition module is arranged on the side of the body fat scale, or the side face of the scale body in the body fat scale. For the convenience of user use, the second fingerprint recognition panel in the second fingerprint recognition module has a first inclination angle with the plumb plane. It should be understood that the first inclination angle can be set to be inclined toward the center direction of the body fat scale or the scale body, so that when the user lets the finger hang naturally, the inward inclination angle of the finger can also allow the user to easily place the finger on the fingerprint recognition module, reducing the difficulty and time of operation and improving the efficiency of obtaining fingerprint images.

[0078] In some embodiments, the first inclination angle can also be set to tilt toward the direction outside the body fat scale or the scale body. For young and / or short users, the users can also easily place their fingers on the fingerprint recognition module, reducing the difficulty and time of the operation and improving the efficiency of obtaining the fingerprint image.

[0079] The first fingerprint recognition panel 211A is provided with a first sensor 211B. It should be understood that the first sensor 211B may be an optical fingerprint sensor or a capacitive fingerprint sensor.

[0080] In some embodiments, in order to improve the recognition speed and accuracy of the fingerprint image, the first sensor is a fusion sensor, including an optical fingerprint sensor and a capacitive fingerprint sensor, which combines the optical fingerprint sensor and the capacitive fingerprint sensor. The optical fingerprint sensor can quickly obtain the overall image of the fingerprint, and the capacitive fingerprint sensor can more accurately capture the detailed features of the fingerprint, such as the ridges and valleys of the fingerprint; the fusion of multiple sensors can not only improve the speed of fingerprint recognition, but also enhance the accuracy, and can quickly and accurately obtain the fingerprint image even when the finger is slightly stained or wet.

[0081] In some embodiments, the body fat scale can also obtain environmental information (such as temperature, humidity, light intensity, etc.) through the application on the terminal device. Therefore, an adaptive fusion algorithm can be used to dynamically adjust the weights of the two sensor data according to different environmental conditions (such as humidity, light intensity) and user fingerprint characteristics (such as dryness and wetness, fingerprint pattern clarity). For example, in an environment with high humidity, increase the weight of the capacitive fingerprint sensor data because the optical fingerprint sensor may have reduced image quality in a humid environment. By continuously adjusting the weight, the fused fingerprint features are made more accurate and the recognition accuracy is improved.

[0082] The first fingerprint recognition module also includes a first indicator light, and the first indicator light is disposed on the first surface of the body fat scale. When the first indicator light is illuminated in a preset mode, it is used to remind the user that the corresponding first fingerprint recognition module is the most suitable fingerprint recognition module to be used, and the first indicator light is always on or flashing to guide the user to use the module to enter the fingerprint.

[0083] For example, the first indicator light can be set on the periphery of the first fingerprint recognition panel, or on the periphery of the first fingerprint recognition module on the first surface; the shape of the first indicator light can be ring-shaped, dot-shaped, strip-shaped, etc.; the embodiment of the present application does not limit the position and shape of the first indicator light.

[0084] Fig. 9 A schematic diagram of the structure of another body fat scale provided in an embodiment of the present application is shown. Fig. 9 As shown, the first indicator light 211D of the first fingerprint recognition module 211 is disposed at the periphery of the first fingerprint recognition module 211 in a ring shape.

[0085] In some embodiments, the first fingerprint recognition module also includes an LED light source, which emits red light and infrared light. These lights penetrate the skin and blood vessels. When the blood flows, oxygenated hemoglobin and deoxygenated hemoglobin absorb these two lights in different proportions. After the light passes through the blood vessels and is partially absorbed, it is received by the first sensor in the first fingerprint recognition module. Furthermore, by optimizing the signal acquisition and processing circuit of the first sensor, the changes in the pulse wave and the degree of light absorption can be measured.

[0086] Inside the first fingerprint recognition panel of the first fingerprint recognition module, an LED emitting red light and infrared light is integrated as a light source for heart rate measurement.

[0087] It should be understood that the first sensor includes an optical fingerprint sensor and a capacitive fingerprint sensor, that is, the first fingerprint recognition module has a photoelectric detection function for capturing fingerprint images. After adding an LED light source, the existing photoelectric sensor can be used to detect the absorption and reflection changes of red light and infrared light when passing through the blood vessels of the finger, and then obtain the photoelectric volume pulse wave signal related to the heart rate.

[0088] It should be understood that the second fingerprint recognition panel is provided with a second sensor, and the second sensor may be an optical fingerprint sensor or a capacitive fingerprint sensor; the second sensor may also include an optical fingerprint sensor and a capacitive fingerprint sensor.

[0089] The second fingerprint recognition module also includes a second indicator light, which, when emitting light in a preset mode, is used to remind the user that the corresponding second fingerprint recognition module is the most suitable fingerprint recognition module to use, and the second indicator light is always on or flashing to guide the user to use the module to enter the fingerprint. The embodiment of the present application does not limit the position and shape of the second indicator light.

[0090] It should be understood that the second fingerprint recognition module is set on the side of the body fat scale, which is not easy to find from the user's perspective (looking down), so the second indicator light is set on the surface of the body fat scale and corresponds to the second fingerprint recognition module on the side. Fig. 9As shown, the second indicator light 221A in the second fingerprint recognition module 221 is arranged on the surface of the scale body for the convenience of the user to view, and is connected to the second fingerprint recognition module in a strip shape.

[0091] In some embodiments, the second fingerprint recognition module further includes an LED light source, and reference may be made to the above description of the LED light source in the first fingerprint module, which will not be repeated here.

[0092] In some embodiments, the second fingerprint recognition modules can be arranged in a circle around the body fat scale, and each second fingerprint recognition module is provided with a corresponding second indicator light. When the target fingerprint recognition module is determined to be one of the second fingerprint recognition modules, the corresponding second indicator light will automatically light up, guiding the user to quickly place the finger in the correct position, so as to obtain the fingerprint image more quickly. At the same time, different colors of lights can be set according to different users to increase the personalized experience.

[0093] It should be understood that an LED light source is added to each fingerprint recognition module to monitor the user's heart rate. The heart rates of different users are also different. Therefore, without affecting the fingerprint recognition function, adding monitoring of the user's heart rate data can be used to obtain the user's historical usage data, and can also be used to optimize the preset recognition model, etc.

[0094] In other embodiments, the identity recognition unit further includes a facial recognition module, which is disposed on the surface of the handle, and a camera disposed at the top of the display in the handle can accurately capture the features of the user's face. For example, the camera can use infrared fill light technology to clearly and accurately capture the features of the user's face even in a dimly lit environment.

[0095] When the user stands on the body fat scale, the camera is activated to obtain the user's facial image. The facial recognition algorithm in the control unit has been deeply optimized and can quickly identify the user's identity. It forms a double insurance with the fingerprint recognition module, greatly improving the accuracy and convenience of identity recognition. Whether you wake up bleary-eyed in the morning or sweating profusely after exercise, the facial recognition module can work stably, bringing users a smooth user experience and easily achieving accurate data matching when multiple users share the body fat scale.

[0096] Combine the following Figure 10 to Figure 11 The control method of the body fat scale provided in the embodiment of the present application is described in detail. It should be noted that if there are substantially the same results, the method of the present application is not limited to Figure 10 to Figure 11 The process sequence shown is limited. The embodiment of the present application also provides a control method of a body fat scale, which is applied to the above-mentioned body fat scale. Fig.10 A schematic diagram of a control method of a body fat scale provided in an embodiment of the present application is shown. Fig.10 As shown, the control method of the body fat scale includes the following steps: S310: When the body fat scale is used a preset number of times and it is recognized that the user is in a stable state on the body fat scale, first initial data of the user is obtained.

[0097] The first initial data includes bioimpedance data and pressure distribution data. It should be understood that the bioimpedance data is obtained through the electrode module, and the pressure distribution data is obtained through the pressure sensor module.

[0098] The preset first number is determined by the body fat scale developers during the development process. For example, the preset number can be 10 times or 20 times. It should be understood that the preset number will be controlled to meet the learning ability and be as small as possible to improve the user experience.

[0099] It should be understood that from the first use until the number of uses reaches a preset number, the body fat scale is in learning mode. By learning from different users of the body fat scale, the user's historical usage data and user preference analysis data are obtained.

[0100] The body fat scale is provided with a first-time use guidance process. When the body fat scale is used for the first time, a communication connection is established between the mobile phone and the eight-electrode body fat scale through the application on the mobile phone, and the user is required to enter basic data in the application, such as gender, age, height, etc., so as to better perform data matching and analysis later. In addition, the user is prompted to use each fingerprint recognition module in turn to enter the fingerprint image, record the user's historical usage data, such as the fingerprint image and the corresponding module usage, and analyze the features in the fingerprint images collected by different fingerprint recognition modules, such as fingerprint image clarity, fingerprint patterns, number of feature points, etc. For example, on a body fat scale with three fingerprint recognition modules (for example, a first fingerprint recognition module and two second fingerprint recognition modules), the user enters the fingerprint image in each fingerprint recognition module according to the prompt, enters the fingerprint image 3 times in total, and analyzes the features in the fingerprint image collected each time.

[0101] During the process of using the body fat scale from the second time to the Nth time (the preset number of times is N+1 times), the frequency of use of the fingerprint recognition module is recorded through historical usage data, so as to facilitate the subsequent optimization of the preset recognition model and learn which fingerprint recognition module the user is accustomed to using to enter fingerprints, the user's pressure distribution data, the user's heart rate data, etc.

[0102] It should be understood that each sensor in each fingerprint recognition module is initialized before each use of the body fat scale. For example, for an optical fingerprint sensor, parameters such as exposure time and image resolution are set; for a capacitive fingerprint sensor, the sensing capacitance range is calibrated.

[0103] In some embodiments, the preset usage time of the body fat scale may be a time determined by the body fat scale developers. For example, the preset usage time may be one week or one month.

[0104] It should be noted that the user is in a stable state on the body fat scale by evenly distributing multiple pressure sensors inside the body fat scale. When the user steps on the body fat scale, the pressure sensors start working. If the pressure values ​​detected by each pressure sensor change within a very small range (for example, the fluctuation does not exceed 5%) within a period of time (such as 1-3 seconds), and the overall pressure distribution is relatively uniform and stable, it can be determined that the user's posture is stable. For example, when the user stands steadily with both feet, the pressure distribution data measured by the pressure sensors on the left and right sides and the front and back positions are stable, indicating that the user's posture is stable.

[0105] In some embodiments, an accelerometer can also be embedded in the body fat scale to sense the acceleration changes of the body fat scale in all directions. When the user stands on the scale, the accelerometer collects data in real time. When the acceleration changes in the three axes of X, Y, and Z are close to zero and last for a period of time (such as 1-2 seconds), it means that the body fat scale is in a stationary state, and it can be determined that the user is in a stable state. For example, the user's body is no longer shaking, the body fat scale has no displacement or tilt, and the accelerometer data tends to be stable.

[0106] In other embodiments, the data of the pressure sensor and the acceleration sensor can be integrated to analyze the stability of the pressure distribution data and to perform weighted calculation and logical judgment in combination with the acceleration change. Through the in-depth analysis of multi-source data by the preset algorithm, it can be more accurately determined whether the user is in a stable state, effectively avoiding the possible misjudgment of a single sensor and ensuring the accuracy of the measurement results.

[0107] S320: Input the first initial data, historical usage data, and user preference analysis data into a preset recognition model to determine a target fingerprint recognition module that is suitable for the user.

[0108] It should be understood that the historical usage data and user preference analysis data are acquired, analyzed and stored when the number of times the body fat scale is used does not reach a preset number.

[0109] Therefore, after obtaining the first initial data, the first initial data, historical usage data, and user preference analysis data are collectively used as input data and input into the preset recognition model to obtain a target fingerprint recognition module adapted to the user. The target fingerprint recognition module is the first fingerprint recognition module or the second fingerprint recognition module.

[0110] It should be understood that the preset recognition model in step 320 is a trained model and is optimized through usage data corresponding to the number of times the user uses the model that does not reach the preset number.

[0111] The preset recognition model is a machine learning model, which can be constructed using algorithms such as decision trees and neural networks. Taking the neural network model as an example, it consists of an input layer, a hidden layer, and an output layer. The input layer is responsible for receiving the first initial data, historical usage data, and user preference analysis data. These data are processed through complex calculations and feature extraction by a large number of neurons in the hidden layer, and finally the target fingerprint recognition module results adapted to the user are output in the output layer.

[0112] The body fat scale learns the user's usage habits, fingerprint placement methods, and fingerprint entry preferences through a preset recognition model, and automatically adjusts the parameters and strategies of the preset recognition model to adapt to the operating characteristics of different users, thereby obtaining the user's fingerprint image more quickly and accurately in subsequent use.

[0113] The preset recognition model deployed on the body fat scale is a trained model, and its input data includes at least the first initial data, historical usage data, and user preference analysis data, and may also include heart rate data, etc.

[0114] During the development of the body fat scale, a large number of samples containing various types of data and corresponding target fingerprint recognition module annotations are collected. The sample data is input into the preset recognition model, which processes the input data according to the rules of the selected algorithm and outputs the prediction results. Then, the prediction results are compared with the actual annotations, and the difference between the two is measured by calculating the loss function. Based on the loss function value, the back propagation algorithm is used to adjust the connection weights of the neurons in each layer of the preset recognition model to reduce the loss value. After multiple rounds of iterative training, the preset recognition model is continuously optimized until satisfactory accuracy, recall rate and other indicators are achieved on the verification set, so that the preset recognition model can be used to determine the target fingerprint recognition module that is suitable for the user.

[0115] It should be understood that the optimization is performed based on the usage data corresponding to the number of times the user uses the body fat scale that does not reach the preset number; that is, when the number of times the body fat scale is used does not reach the preset number and it is recognized that the user is in a stable state on the body fat scale, the model is optimized. Before 310, the control method of the body fat scale also includes the following (1)-(3): (1) Obtaining a second fingerprint image and second initial data of the user, wherein the second fingerprint image is an image collected by the first fingerprint recognition module and / or the second fingerprint recognition module.

[0116] It should be understood that if the body fat scale is used for the first time, the second fingerprint image includes an image collected by the first fingerprint recognition module and an image collected by the second fingerprint recognition module.

[0117] If the body fat scale is used for the second to Nth time (the preset number of times is N+1 times), the second fingerprint image includes an image collected by the first fingerprint recognition module and / or an image collected by the second fingerprint recognition module.

[0118] For the sake of ease of distinction, the measurement data obtained by the measurement unit when the body fat scale is used less than the preset number of times is called the second initial data; the measurement data obtained by the measurement unit when the body fat scale is used more than the preset number of times is called the first initial data.

[0119] Therefore, the second initial data also includes bioimpedance data and pressure distribution data. It should be understood that the bioimpedance data is obtained through the electrode module, and the pressure distribution data is obtained through the pressure sensor module.

[0120] (2) Analyze the second fingerprint image and the second initial data to obtain the user's historical usage data and user preference analysis data.

[0121] When the body fat scale is used less than the preset number of times, the preset recognition model is optimized. Therefore, the second fingerprint image and the second initial data need to be analyzed in the process: first, the corresponding fingerprint recognition module, usage time, etc. are recorded for each use, and the second fingerprint image is feature extracted to identify the user's unique fingerprint image clarity, fingerprint pattern, number of feature points, etc.; so that the preset recognition model can be optimized to be more suitable for the user through the second fingerprint image. Then, the user's historical usage data and user preference analysis data are obtained.

[0122] Among them, by associating the current second fingerprint image with the second initial data, the corresponding fingerprint recognition module and other information, and comparing, integrating and statistically analyzing the data accumulated by the same fingerprint recognition in the past, the user's historical usage data can be obtained, covering information such as the frequency of use in different time periods.

[0123] During multiple uses, the user's various preference settings are recorded, such as the commonly used fingerprint recognition module, whether they are used to automatically adjusting the angle, etc. The data obtained each time is deeply integrated with the stored historical usage data. By comparing and correlating data at different stages, the body fat scale can further understand the user's behavior patterns and preferences, analyze the selection tendency of the body fat scale function and the selection tendency of the fingerprint recognition module, improve the user preference analysis data, and then obtain comprehensive and detailed user preference analysis data.

[0124] (3) Optimizing the preset recognition model based on the second fingerprint image, the second initial data, the historical usage data, and the user preference analysis data to obtain an optimized preset recognition model.

[0125] In the daily use of body fat scales, the intelligence of body fat scales will gradually advance as the number of times users use them increases. Every time a user completes a measurement, the body fat scale will collect the second fingerprint image to accurately identify the user.

[0126] The body fat scale uses the second fingerprint image, the second initial data, the historical usage data, and the user preference analysis data as input data to optimize the preset recognition model in an all-round way. In the continuous iterative operation, the preset recognition model gradually adjusts the internal parameters to improve the recognition accuracy of the user's individual characteristics and behavioral habits, thereby obtaining an optimized preset recognition model, and then in the subsequent use process, provides users with a more accurate target fingerprint recognition model and provides users with personalized services.

[0127] In some embodiments, after the control unit determines the target fingerprint recognition module that is suitable for the user, it generates prompt information based on the target fingerprint recognition module, and the prompt information is used to instruct the user to enter the fingerprint through the target fingerprint recognition module. It should be understood that the prompt information can be presented in different forms to achieve the purpose of prompting the user.

[0128] In some embodiments, when the prompt information indicates that the first fingerprint recognition module is the target fingerprint recognition module, the first indicator light is driven to illuminate based on a preset mode; when the prompt information indicates that the second fingerprint recognition module is the target fingerprint recognition module, the first indicator light is driven to illuminate based on a preset mode; wherein the preset mode includes a constant light mode or a flashing mode.

[0129] In some embodiments, the body fat scale also includes a voice unit, including a speaker. The prompt information can also remind the user to place the finger on the designated target fingerprint recognition module by voice, and guide the user how to correctly place the finger to obtain the best recognition effect. For users with poor eyesight or unfamiliar with the operation, the voice prompt can greatly improve the convenience of obtaining fingerprint images. In other embodiments, the voice unit also includes a microphone, and voice interaction with the user is achieved by adding a corresponding voice recognition model to the control unit.

[0130] In some embodiments, "XX fingerprint recognition area is the best usage area" is displayed on the display of the body fat scale or the corresponding application. It should be understood that the XX fingerprint recognition area here corresponds to the target fingerprint recognition module on the body fat scale, and the text prompting the user is easy to understand.

[0131] It should be understood that the preset recognition model will continue to learn and optimize during use, constantly learning changes in users' fingerprint features, usage habits and preferences, etc., dynamically optimizing the preset recognition model and updating the recommended results of the best fingerprint recognition module.

[0132] S330: In response to the user entering a fingerprint operation through the target fingerprint recognition module, a first fingerprint image of the user is acquired, and a first identifier corresponding to the first fingerprint image is determined.

[0133] The target fingerprint recognition module is the first fingerprint recognition module or the second fingerprint recognition module, and the user is guided to input the fingerprint image through the target fingerprint recognition module.

[0134] It should be noted that the first fingerprint image is identified and analyzed based on the user information stored in the body fat scale. If the first fingerprint image is identified, the first identifier corresponding to the first fingerprint image is determined.

[0135] It should be understood that the terminal device will send the user's basic data to the body fat scale, and match it with the second fingerprint image obtained when the number of times of use does not reach the preset number, and store it in the body fat scale; in order to distinguish different users, an identifier can be set for each user. When it is identified that the first fingerprint image belongs to a stored user, the identifier of the user is used as the first identifier corresponding to the first fingerprint image.

[0136] In some embodiments, after the first fingerprint image is entered, the user can be informed of the recognition status in different forms during the recognition of the first fingerprint image. For example, a progress bar can be displayed on the display of the body fat scale, or a voice prompt "Fingerprint is being recognized, please wait" can be pushed through the application. If the first fingerprint image is successfully recognized, it can be displayed "Recognition is successful, measuring indicator data"; if the recognition fails, a specific reason can also be given, such as "The fingerprint is blurred, please re-place it" or "The fingerprint is not registered, please register first".

[0137] S340: Based on the first identifier, establish a matching relationship between the first initial data and basic data corresponding to the first identifier.

[0138] It should be understood that the first identifier is used to identify the user, and based on the first identifier, a matching relationship between the first initial data and the basic data corresponding to the first identifier can be established to facilitate subsequent data analysis and determination of indicator data.

[0139] S350: Determine the indicator data of the user based on the first initial data and the basic data corresponding to the first identifier.

[0140] The control method of the body fat scale provided in the embodiment of the present application obtains the first initial data of the user when the body fat scale is used a preset number of times and it is recognized that the user is in a stable state on the body fat scale; the first initial data, as well as the corresponding historical usage data and user preference analysis data are input into a preset recognition model to determine a target fingerprint recognition module adapted to the user; in response to the user entering a fingerprint operation through the target fingerprint recognition module, the user's first fingerprint image is obtained, and a first identifier corresponding to the first fingerprint image is determined; based on the first identifier, a matching relationship between the first initial data and the basic data corresponding to the first identifier is established, and the user's usage habits are learned during the user's use, etc., so as to adapt to the operating characteristics of different users and quickly and accurately determine the user's identity.

[0141] In some embodiments, after the first fingerprint image is acquired, there may still be unrecognized situations in the process of identifying and analyzing it. In order to improve the fault tolerance rate of the solution, user experience, etc., Fig.11 The schematic diagram of the process of analyzing a control method of a body fat scale provided in an embodiment of the present application is shown as follows: Fig.11 As shown, in response to the user entering a fingerprint operation through the target fingerprint recognition module in step 330, after obtaining the user's first fingerprint image, the following steps are also included: S410: Identify and analyze the first fingerprint image based on the user information stored in the body fat scale.

[0142] It should be understood that the terminal device will send the user's basic data to the body fat scale, and match it with the second fingerprint image obtained when the number of uses does not reach the preset number, and store it in the body fat scale; in order to distinguish different users, an identifier can be set for each user. When it is identified that the first fingerprint image belongs to a stored user, the identifier of the user is used as the first identifier corresponding to the first fingerprint image; when it is identified that the first fingerprint image does not belong to a stored user, it indicates that the first fingerprint image is not recognized.

[0143] S420: If the first fingerprint image is not recognized, the features of the first fingerprint image are respectively matched with the fingerprint features collected by the first fingerprint recognition module and the second fingerprint recognition module to obtain corresponding similarities.

[0144] It should be understood that when the fingerprint image currently entered by the user fails to be recognized, the switching process of the fingerprint recognition module will be started. The database containing the user's fingerprint features recorded in the previously optimized preset recognition model is quickly retrieved from the storage unit. The database stores in detail various feature information when the user collects fingerprints in each fingerprint recognition area, including fingerprint pattern clarity, number of feature points, etc.

[0145] Using a similarity matching algorithm, the features of the current fingerprint image that failed to be recognized (i.e., the first fingerprint image) are compared one by one with the fingerprint features already collected by each fingerprint recognition module, and the similarity between each fingerprint recognition module and the first fingerprint image is calculated. For example, the Euclidean distance algorithm or the cosine similarity algorithm is used to quantitatively evaluate the similarity between the fingerprint features.

[0146] After calculating the similarity, the fingerprint recognition modules are sorted in descending order according to the similarity scores to obtain the similarity ranking.

[0147] S430: Use the fingerprint recognition module with the highest similarity as the updated target fingerprint recognition module.

[0148] It should be understood that the fingerprint recognition module with the highest similarity score in the similarity sorting is the fingerprint recognition module that best matches the fingerprint feature of the current user, and the target fingerprint recognition module is then updated.

[0149] And, generate corresponding prompt information to prompt the user, for example, issue a clear prompt to the user through the display of the body fat scale or the application connected thereto. The prompt content may include the information of the fingerprint recognition module recommended to switch to, and may also briefly explain the reason for the recommendation, such as "The pattern characteristics of your fingerprint have the highest matching degree with the data collected by the XX fingerprint recognition module, and it is recommended to switch to the XX fingerprint recognition module for recognition."

[0150] It should be understood that after the target formwork identification module is updated, the above step 330 is performed again.

[0151] To ensure that users can conveniently switch between fingerprint recognition modules, corresponding interactive guidance can also be provided in the prompt interface. For example, an image is generated on the application to prompt the user of the corresponding position of the target fingerprint recognition module; on the display of the body fat scale, the indicator light is used to guide the user to place the finger on the recommended fingerprint recognition module. In addition, the operation data of each fingerprint recognition module switch can be recorded to facilitate the subsequent optimization of the preset recognition model and continuously improve the recognition efficiency and accuracy.

[0152] It should be understood that in some embodiments, the universal connection mechanism in the first fingerprint recognition module further includes an angle sensor and a motor. In this case, in step 320, after determining the target fingerprint recognition module adapted to the user, the following (1)-(3) are also included: (1) If the target fingerprint recognition module is the first fingerprint recognition module, historical usage data is analyzed to determine the target angle of the first fingerprint recognition panel.

[0153] The historical usage data includes the usage frequency of the first fingerprint recognition module.

[0154] Please refer to the above description of historical usage data acquisition and analysis, which will not be repeated here.

[0155] (2) When the user enters the fingerprint, the initial angle corresponding to the first fingerprint recognition panel is obtained by the angle sensor. The angle sensor is used to accurately measure and feedback the rotation angle of the first fingerprint recognition panel.

[0156] (3) Control the motor to adjust the angle of the first fingerprint recognition panel to a target angle.

[0157] For the first fingerprint recognition panel with adjustable angle, the panel angle can be adjusted according to the position and angle of the user's fingerprint through the motor drive algorithm built into the control unit, that is, the first fingerprint recognition panel corresponding to the first sensor is adjusted to the angle corresponding to the user's habit, so that the first sensor and the user's finger can fit better.

[0158] In some embodiments, the deviation between the user's finger placement angle and the current angle of the first fingerprint recognition panel can also be detected, and the first fingerprint recognition panel can be adjusted accordingly. For example, when it is detected that the deviation between the user's finger placement angle and the current angle of the panel exceeds 15°, the angle value that needs to be adjusted is calculated, and the motor is controlled to adjust the panel to a suitable angle to improve the fingerprint collection quality and recognition success rate.

[0159] When the motor is controlled to adjust the angle of the first fingerprint recognition panel, the user can be informed that the angle adjustment is in progress through the flashing of the first indicator light or voice prompt. For example, a voice prompt of "adjusting the angle of the fingerprint recognition panel, please keep your finger stable" is issued, and at the same time, the first indicator light around the fingerprint recognition area flashes quickly to let the user know the current operation.

[0160] The control method of the body fat scale provided in the embodiment of the present application can also drive the fingerprint recognition panel to rotate through the motor-driven universal connection mechanism to achieve automatic angle adjustment. This structure can achieve multi-angle adjustment in a wide range to meet the needs of different users (such as different heights, different usage habits, etc.) to enter fingerprints in a comfortable posture.

[0161] In some embodiments, the body fat scale can also obtain heart rate data through the LED light source and the first sensor, that is, the first initial data also includes heart rate data, and the second initial data also includes heart rate data. Without affecting the fingerprint recognition function, a special heart rate signal processing algorithm can be added to the control unit to filter, amplify, extract features, etc. of the collected photoelectric volume pulse wave signal to calculate the user's heart rate data.

[0162] It should be understood that heart rate data can be used to obtain historical usage data of the user.

[0163] It should be understood that for body fat scales with LED light sources, the corresponding preset recognition model will add heart rate data during the training process. Since more input data can cover more features and changes, the preset recognition model can learn more complex and subtle patterns and rules, reduce the risk of overfitting, and improve the generalization ability on unknown data to improve the accuracy of the preset recognition model. In addition, during the optimization and use of the preset recognition model, the heart rate data is used as input data to optimize the preset recognition model, so that the optimized preset recognition model can more accurately output the target fingerprint recognition module results that are suitable for the user.

[0164] Therefore, in step 320, the heart rate data is also used as input data and input into a preset recognition model to determine a target fingerprint recognition module that is suitable for the user.

[0165] In some embodiments, the control method of the body fat scale also includes: when the user's finger is close to the fingerprint recognition module but has not yet fully touched it, a weak signal is captured by the sensor (first sensor or second sensor) corresponding to the fingerprint recognition module, the signal is preliminarily analyzed to determine whether it is a valid fingerprint, and related recognition preparations are started in advance, so that when the finger is fully in contact, the fingerprint image can be acquired and recognized more quickly.

[0166] It should be understood that the above is an example of an application scenario and does not limit the application scenario of the present application.

[0167] It should be understood that the above examples are intended to help those skilled in the art understand the embodiments of the present application, rather than to limit the embodiments of the present application to the specific numerical values ​​or specific scenarios illustrated. Those skilled in the art can obviously make various equivalent modifications or changes based on the above examples, and such modifications or changes also fall within the scope of the embodiments of the present application.

[0168] Combination of the above Figures 2 to 11 , describes in detail the body fat scale and the control method of the body fat scale of the embodiment of the present application, and will be combined with Fig.12 , describes the device embodiment of the present application in detail. It should be understood that the control device in the embodiment of the present application can execute the control methods of various body fat scales in the aforementioned embodiments of the present application, that is, the specific working processes of the following various products can refer to the corresponding processes in the aforementioned method embodiments.

[0169] Fig.12 A schematic diagram of a control device for a body fat scale provided in an embodiment of the present application is shown. Fig.12 As shown, the control device 500 of the body fat scale can execute Figure 10 to Figure 11 The control method of the body fat scale shown in the figure; the control device 500 of the body fat scale includes an acquisition module 510, an analysis module 520 and an identification module 530, wherein: The acquisition module 510 is used to acquire the user's first initial data when the body fat scale is used a preset number of times and it is recognized that the user is in a stable state on the body fat scale. The first initial data includes bioimpedance data and pressure distribution data.

[0170] The analysis module 520 is used to input the first initial data, the historical usage data, and the user preference analysis data into a preset recognition model to determine a target fingerprint recognition module that is suitable for the user.

[0171] The identification module 530 is used to obtain the user's first fingerprint image and determine the first identifier corresponding to the first fingerprint image in response to the user entering a fingerprint operation through the target fingerprint identification module, wherein the target fingerprint identification module is the first fingerprint identification module or the second fingerprint identification module; the identification module 530 is also used to establish a matching relationship between the first initial data and the basic data corresponding to the first identifier based on the first identifier, and determine the user's indicator data.

[0172] Each module of the control device 500 of the body fat scale can respectively execute the corresponding steps in the above method embodiment, so each module will not be described in detail here. Please refer to the description of the above corresponding steps for details.

[0173] It should be noted that the control device 500 of the body fat scale is implemented in the form of a functional module. The term "module" here can be implemented in the form of software and / or hardware, and is not specifically limited to this.

[0174] For example, a "module" may be a software program, a hardware circuit, or a combination of the two that implements the above functions. The hardware circuit may include an application specific integrated circuit (ASIC), an electronic circuit, a processor (such as a shared processor, a dedicated processor, or a group processor, etc.) and a memory for executing one or more software or firmware programs, a combined logic circuit, and / or other suitable components that support the described functions.

[0175] Fig.13 A structural schematic diagram of a body fat scale provided in the present application is shown. Fig.13 The dotted line in the figure indicates that the unit or the module is optional. The body fat scale 600 can be used to implement the control method of the body fat scale described in the above method embodiment.

[0176] The body fat scale 600 includes one or more processors 601, which can support the body fat scale 600 to implement the control method of the body fat scale in the method embodiment. The processor 601 can be a general processor or a special processor. For example, the processor 601 can be a central processing unit (CPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices.

[0177] The processor 601 can be used to control the body fat scale 600, execute the software program, and process the data of the software program. The body fat scale 600 can also include a communication unit 605 to realize the input (reception) and output (transmission) of signals.

[0178] The body fat scale 600 may include one or more memories 602 on which a program 604 is stored. The program 604 can be executed by the processor 601 to generate instructions 603, so that the processor 601 executes the control method of the body fat scale described in the above method embodiment according to the instructions 603.

[0179] The present application also provides a computer program product, which implements the control method of the body fat scale of any method embodiment of the present application when executed by the processor 601. The computer program product can be stored in the memory 602, for example, a program 604, which is finally converted into an executable target file that can be executed by the processor 601 after preprocessing, compiling, assembling and linking.

[0180] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a computer, the control method of the body fat scale of any method embodiment in the present application is implemented. The computer program can be a high-level language program or an executable target program. The computer-readable storage medium is, for example, the memory 602.

[0181] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic; for example, the division of units is only a logical function division, and there may be other division methods in actual implementation. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection of devices or units, which may be electrical, mechanical or other forms.

[0182] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A body fat scale, characterized in that: It includes an identification unit, a measuring unit, a control unit and a wireless communication unit, wherein the control unit is in communication connection with the identification unit, the measuring unit and the wireless communication unit, wherein: The identity recognition unit includes a first fingerprint recognition module and a second fingerprint recognition module, wherein the first fingerprint recognition module is arranged on the first surface of the body fat scale, and the first fingerprint recognition module includes a first fingerprint recognition panel with an adjustable angle and a first sensor; the second fingerprint recognition module is arranged on the side of the body fat scale, and the second fingerprint recognition module includes a second fingerprint recognition panel and a second sensor, and the second fingerprint recognition panel has a first inclination angle with the plumb plane; The measuring unit includes an electrode module and a pressure sensor module; The control unit is configured as: When the body fat scale has been used for a preset number of times and it is recognized that the user is in a stable state on the body fat scale, first initial data of the user is acquired, where the first initial data includes bioimpedance data and pressure distribution data; Inputting the first initial data, historical usage data, and user preference analysis data into a preset recognition model to determine a target fingerprint recognition module adapted to the user; In response to the user entering a fingerprint operation through the target fingerprint recognition module, acquiring a first fingerprint image of the user and determining a first identifier corresponding to the first fingerprint image, wherein the target fingerprint recognition module is the first fingerprint recognition module or the second fingerprint recognition module; Based on the first identifier, a matching relationship between the first initial data and basic data corresponding to the first identifier is established, and indicator data of the user is determined.

2. The body fat scale according to claim 1, characterized in that: The first fingerprint recognition module further includes a universal connection mechanism, which is used to flexibly connect the first fingerprint recognition panel to the body fat scale. The first fingerprint recognition panel is provided with the first sensor, and the second fingerprint recognition panel is provided with the second sensor.

3. The body fat scale according to claim 2, characterized in that: The universal connection mechanism further includes an angle sensor and a motor. After determining the target fingerprint recognition module adapted to the user, the control unit is further configured to: If the target fingerprint recognition module is the first fingerprint recognition module, analyzing the historical usage data to determine the target angle of the first fingerprint recognition panel, wherein the historical usage data includes the usage frequency of the first fingerprint recognition module and the initial angle corresponding to the first fingerprint recognition panel obtained by the angle sensor when the user enters the fingerprint; The motor is controlled to adjust the angle of the first fingerprint recognition panel to the target angle.

4. The body fat scale according to claim 2, characterized in that: After the control unit acquires the first fingerprint image of the user in response to the user entering a fingerprint operation through the target fingerprint recognition module, the control unit is further configured to: Performing recognition and analysis on the first fingerprint image based on the user information stored in the body fat scale; If the first fingerprint image is not recognized, the features of the first fingerprint image are respectively matched with the fingerprint features collected by the first fingerprint recognition module and the second fingerprint recognition module to obtain corresponding similarities; The fingerprint recognition module with the highest similarity is used as the updated target fingerprint recognition module.

5. The body fat scale according to claim 1, characterized in that: When the number of times the body fat scale is used does not reach a preset number and it is recognized that the user is in a stable state on the body fat scale, the control unit is further configured to: Acquire a second fingerprint image and second initial data of the user, wherein the second fingerprint image is an image collected by the first fingerprint recognition module and / or the second fingerprint recognition module; Analyze the second fingerprint image and the second initial data to obtain the user's historical usage data and user preference analysis data; The preset recognition model is optimized based on the second fingerprint image, the second initial data, the historical usage data and the user preference analysis data to obtain the optimized preset recognition model.

6. The body fat scale according to claim 1, characterized in that: The first fingerprint recognition module further includes a first indicator light, and the second fingerprint recognition module further includes a second indicator light; after the control unit determines the target fingerprint recognition module adapted to the user, it is further configured to: and generating prompt information, wherein the prompt information is used to instruct the user to enter a fingerprint through the target fingerprint recognition module; When the prompt information indicates that the first fingerprint recognition module is the target fingerprint recognition module, driving the first indicator light to emit light based on a preset mode; When the prompt information indicates that the second fingerprint recognition module is the target fingerprint recognition module, driving the first indicator light to emit light based on a preset mode; The preset mode includes a constant light mode or a flashing mode.

7. The body fat scale according to any one of claims 1 to 6, characterized in that: The first fingerprint recognition module also includes a first LED light source, and the first sensor includes an optical fingerprint sensor and a capacitive fingerprint sensor; The control unit is further configured to: The heart rate data of the user is obtained, and the heart rate data is used to obtain the historical usage data of the user, to optimize the preset recognition model, and to input into the preset recognition model to determine a target fingerprint recognition module adapted to the user.

8. The body fat scale according to any one of claims 1 to 6, characterized in that: The body fat scale comprises a handle and a scale body; The identity recognition unit further includes a facial recognition module. The first fingerprint recognition module and the facial recognition module are arranged on the second surface of the handle, and the second fingerprint recognition module is arranged on the side of the scale body.

9. The body fat scale according to any one of claims 1 to 6, characterized in that: The first sensor includes an optical fingerprint sensor and a capacitive fingerprint sensor, and the second sensor includes an optical fingerprint sensor and a capacitive fingerprint sensor.

10. A method for controlling a body fat scale, characterized in that: Applied to the body fat scale according to any one of claims 1 to 9 above, the control method of the body fat scale comprises: When the body fat scale has been used for a preset number of times and it is recognized that the user is in a stable state on the body fat scale, first initial data of the user is acquired, where the first initial data includes bioimpedance data and pressure distribution data; Inputting the first initial data, historical usage data, and user preference analysis data into a preset recognition model to determine a target fingerprint recognition module adapted to the user; In response to the user entering a fingerprint operation through the target fingerprint recognition module, acquiring a first fingerprint image of the user and determining a first identifier corresponding to the first fingerprint image, wherein the target fingerprint recognition module is the first fingerprint recognition module or the second fingerprint recognition module; Based on the first identifier, a matching relationship between the first initial data and basic data corresponding to the first identifier is established, and indicator data of the user is determined.

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